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An adaptive sampling strategy for Kriging metamodel based on Delaunay triangulation and TOPSIS

机译:基于Delaunay三角测量和Topsis的Kriging Metomodel的自适应采样策略

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摘要

Metamodels have been widely used in engineering design and optimization. Sampling method plays an important role in the constructing of metamodels. This paper proposes an adaptive sampling strategy for Kriging metamodel based on Delaunay triangulation and TOPSIS (KMDT). In the proposed KMDT, Delaunay triangulation is employed to partition the design space according to current sample points. The area of each partitioned triangle is used to indicate the degree of dispersion of sample points, and the prediction error of Kriging metamodel at each triangle's centroid is used to represent the local error of each triangle region. By calculating the weight of the area and prediction error for each triangle region using the entropy method and TOPSIS, the degree of dispersion of sample points and local errors of metamodel are taken into consideration to make a trade-off between global exploration and local exploitation during the sequential sampling process. As a demonstration, the proposed approach is compared to other three sampling methods using several numerical cases and the modeling of the aerodynamic coefficient for a three-dimensional aircraft. The result reveals that the proposed approach provides more accurate metamodel at the same simulation cost, which is very important in metamodel-based engineering design problems.
机译:元模型已广泛用于工程设计和优化。采样方法在构造元模型中起着重要作用。本文提出了一种基于Delaunay三角测量和Topsis(KMDT)的Kriging Metomodel的自适应采样策略。在提议的KMDT中,采用Delaunay三角测量根据当前采样点进行分区设计空间。每个分区三角形的面积用于指示采样点的色散程度,并且每个三角形的质心在每个三角形的心电图的预测误差用于表示每个三角形区域的局部误差。通过计算使用熵方法和Topsis对每个三角形区域的区域的重量和预测误差,考虑样品点的分散程度和元模型的局部误差,以在全球勘探和当地剥削之间进行权衡顺序采样过程。作为示范,将所提出的方法与其他三种采样方法进行比较,使用若干数值案例和用于三维飞机的空气动力学系数的建模。结果表明,该方法以相同的模拟成本提供更准确的元模型,这在基于元的工程设计问题中非常重要。

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